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Merge pull request #6 from PLN-team/github
Complete CI. Release test
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‎.github/workflows/ci.yml‎

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name: CI
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on:
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push:
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branches:
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- dev
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- github
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pull_request:
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branches:
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- main
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release:
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types: [created]
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jobs:
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black:
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runs-on: ubuntu-22.04
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.9'
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- name: Install Black
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run: pip install black
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- name: Run Black
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run: black --check --verbose -- .
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# pylint:
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# runs-on: ubuntu-22.04
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# needs: black
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# steps:
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# - uses: actions/checkout@v4
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# - name: Set up Python
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# uses: actions/setup-python@v4
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# with:
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# python-version: '3.9'
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# - name: Install Pylint
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# run: |
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# pip install pylint
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# - name: Run Pylint
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# run: find . -type f -name "*.py" | xargs pylint --disable=import-error,missing-module-docstring,invalid-name,not-callable,duplicate-code --load-plugins=pylint.extensions.docparams
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tests:
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runs-on: ubuntu-22.04
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container:
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image: ghcr.io/bastien-mva/docker_image:latest
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steps:
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- uses: actions/checkout@v4
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- name: Install package and run tests
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run: |
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pip install '.[tests]'
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pip install -e .
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jupyter nbconvert Getting_started.ipynb --to python --output tests/test_getting_started
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cd tests
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python _create_readme_getting_started_and_docstrings_tests.py
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rm test_getting_started.py
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pytest test_ar.py
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build_package:
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runs-on: ubuntu-22.04
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.9'
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- name: Install build
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run: pip install build
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- name: Build package
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run: |
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rm -rf dist/
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python -m build
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- name: Upload artifact
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uses: actions/upload-artifact@v4
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with:
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name: dist
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path: dist/
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publish_package:
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runs-on: ubuntu-22.04
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needs:
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- build_package
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- tests
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# if: github.event_name == 'release'
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if: github.event_name == 'release' && github.ref == 'refs/heads/main'
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.9'
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- name: Install Twine
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run: pip install twine
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- name: download artifacts and publish
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uses: actions/download-artifact@v4
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with:
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name: dist
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path: dist/
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- name: Publish package
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env:
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TWINE_USERNAME: __token__
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TWINE_PASSWORD: ${{ secrets.PYPLN_TOKEN }}
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run: python -m twine upload dist/*
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pages:
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runs-on: ubuntu-22.04
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needs: publish_package
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if: github.event_name == 'release' && github.ref == 'refs/heads/main'
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.9'
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- name: Install dependencies
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run: pip install '.[build-doc]'
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- name: Install Pandoc
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run: |
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wget https://github.com/jgm/pandoc/releases/download/1.15.1/pandoc-1.15.1-1-amd64.deb
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sudo dpkg -i pandoc-1.15.1-1-amd64.deb
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- name: Convert README
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run: pandoc README.md --from markdown --to rst -s -o docs/source/readme.rst
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- name: Build docs
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run: |
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pip install .
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make -C docs html
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- name: Deploy to GitHub Pages
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uses: peaceiris/actions-gh-pages@v4
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with:
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github_token: ${{ secrets.GITHUB_TOKEN }}
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publish_dir: ./docs/build/html

‎README.md‎

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> the significance of each variable, their correlations, and the
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> weight of covariates (if available).
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The package documentation can be found [here](https://pln-team.github.io/pyPLNmodels/).
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## Getting started
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[A notebook to get started can be found here](https://github.com/PLN-team/pyPLNmodels/blob/main/Getting_started.ipynb).
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If you need just a quick view of the package, see the quickstart next. Note
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## Statistical description
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For those unfamiliar with the concepts of Poisson or Gaussian random variables,
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it is not necessary to delve into these statistical descriptions. The key
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takeaway is as follows: This package is designed to analyze multi-dimensional count data. It
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effectively extracts significant information, such as
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the mean, the relationships with covariates, and the correlation between count
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For those unfamiliar with Poisson or Gaussian random variables,
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it's not necessary to delve into these statistical concepts. The
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key takeaway is that this package analyzes multi-dimensional count
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data, extracting significant information such as the mean,
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relationships with covariates, and correlations between count
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variables, in a manner appropriate for count data.
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Consider $\mathbf Y$ a count matrix (denoted as `endog` in the package) consisting of $n$ rows and $p$ columns.
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## 👐 Contributing
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Feel free to contribute, but read the [CONTRIBUTING.md](https://forgemia.inra.fr/bbatardiere/pyplnmodels/-/blob/main/CONTRIBUTING.md) first. A public roadmap will be available soon.
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Feel free to contribute, but read the [CONTRIBUTING.md](https://github.com/PLN-team/pyPLNmodels/blob/main/CONTRIBUTING.md) first. A public roadmap will be available soon.
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## ⚡️ Citations
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‎pyPLNmodels/calculations/entropies.py‎

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is for a mixture of gaussians.
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"""
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if latent_prob is None:
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latent_prob = torch.ones((latent_variance).shape)
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latent_prob = torch.ones((latent_variance).shape).to(latent_variance.device)
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product_dimensions = torch.prod(torch.tensor(list(latent_variance.shape[-2:])))
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return (
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1 / 2 * torch.sum((latent_prob.T).unsqueeze(2) * latent_variance)

‎pyPLNmodels/models/plnar.py‎

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self._ar_diff_coef = torch.tensor([0.5]).to(DEVICE)
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else:
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self._ar_diff_coef = torch.ones(self.dim).to(DEVICE) / 2
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self._diff_ortho_components = torch.eye(self.dim)
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self._diff_ortho_components = torch.eye(self.dim).to(DEVICE)
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self._diff_diag_cov = torch.ones(self.dim).to(DEVICE) / (
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self.dim ** (3 / 2)
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)

‎pyPLNmodels/utils/_utils.py‎

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def _trunc_log(tens: torch.Tensor, eps: float = 1e-16) -> torch.Tensor:
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integer = torch.min(
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torch.max(tens, torch.tensor([eps], device=DEVICE)),
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torch.tensor([1 - eps], device=DEVICE),
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torch.max(tens, torch.tensor([eps], device=tens.device)),
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torch.tensor([1 - eps], device=tens.device),
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)
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return torch.log(integer)
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‎tests/_create_readme_getting_started_and_docstrings_tests.py‎

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for i, example in enumerate(examples):
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nb_example = str(i + 1)
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example_filename = (
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f"{directory}/mytest_{filename}_{string_definer}_{nb_example}.py"
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f"{directory}/test_{filename}_{string_definer}_{nb_example}.py"
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)
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try:
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os.remove(example_filename)

‎tests/conftest.py‎

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)
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dict_unfit_models = get_dict_models_unfit()
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dict_fitted_models = get_dict_models_fitted()
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dict_collections_fitted = get_dict_collections_fitted()
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# dict_unfit_models = get_dict_models_unfit()
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# dict_fitted_models = get_dict_models_fitted()
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# dict_collections_fitted = get_dict_collections_fitted()

‎tests/run_readme_docstrings_getting_started.sh‎

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set -e
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run_with_probability() {
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if [ $((RANDOM % 3)) -eq 0 ]; then
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if [ $((RANDOM % 1)) -eq 0 ]; then
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python "$1"
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fi
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}

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